arXiv AI
Oct 2

Partial AUC Maximization from Positive-unlabeled Data

arXiv:2610.00284v1 Announce Type: cross Abstract: The partial area under the receiver operating characteristic curve (pAUC) is an important performance metric for binary classification that summarize...

By Atsutoshi Kumagai, Tomoharu Iwata, Taishi Nishiyama, Hiroshi Takahashi, Kazuki Adachi, Yasuhiro Fujiwara
arXiv Machine Learning
Sep 22

Classification with Abstention Under Class-Conditional Error Constraints

The paper investigates binary classification with abstention under separate class‑conditional error constraints, aiming to minimize abstention while keeping both error types below specified thresholds. It derives the distribution‑free minimax rate of excess abstention risk, introduces surrogate‑loss formulations for computational feasibility with models like neural networks, and provides finite‑sample guarantees for excess surrogate ambiguity risk. The authors also formulate the learning task as a constrained optimization problem, analyze its computational complexity in the convex setting, and empirically evaluate the approach against a competing method on several datasets.

By Mohammadreza M. Kalan, Yuyang Deng, Sanaz Hamidi
arXiv Machine Learning
Jun 16

Imbalanced Classification under Capacity Constraints

arXiv:2605. 03289v2 Announce Type: replace-cross Abstract: Detecting observations from a minority class under severe class imbalance is a central challenge in applications such as fraud detection, medical screening, and industrial quality control.

By Daniel Fraiman, Ricardo Fraiman
arXiv Machine Learning
Sep 11

AUC Maximization from Biased Positive-unlabeled Data with Confidence

The paper introduces a method for maximizing the area under the receiver operating characteristic curve (AUC) when only biased positive and unlabeled (PU) data are available. It leverages confidence scores—probabilities that an instance is positive—associated with a small set of labeled positives to derive an AUC risk estimator that accounts for bias. Experiments on eight real-world datasets demonstrate the method’s effectiveness.

By Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Kazuki Adachi, Yasuhiro Fujiwara
arXiv Statistics ML
Oct 21

Optimal aggregation in multiclass classification

arXiv:2610.28025v1 Announce Type: cross Abstract: This paper studies the problem of aggregation in multiclass classification, generalizing existing results for binary classification. Given a finite d...

By Felix Abramovich, Or Davidovitch-Servadio